NII-UIT: A Tool for Known Item Search by Sequential Pattern Filtering

  • Thanh Duc Ngo
  • Vu Hoang Nguyen
  • Vu Lam
  • Sang Phan
  • Duy-Dinh Le
  • Duc Anh Duong
  • Shin’ichi Satoh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8326)

Abstract

This paper presents an interactive tool for searching a known item in a video or a video archive. To rapidly select the relevant segment, we use query patterns formulated by users for filtering. The patterns can be formulated by drawing color sketches or selecting predefined concepts. Especially, our tool support users to define patterns for sequences of consecutive segments, for instance, sequences of occurrences of concepts. Such patterns are called sequential patterns, which are more powerful to describe users’ search intention. Besides that, the user interface is organized following a coarse-to-fine manner, so that users can quickly scan the set of candidate segments. By using color-based and concept-based filters, our tool can deal with both visual and descriptive known item search.

Keywords

Video Browser Showdown Known Item Search Concept Filtering Sequential Pattern 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Le, D.-D., Lam, V., Ngo, T.D., Tran, V.Q., Nguyen, V.H., Duong, D.A., Satoh, S.: NII-UIT-VBS: A Video Browsing Tool for Known Item Search. In: Li, S., El Saddik, A., Wang, M., Mei, T., Sebe, N., Yan, S., Hong, R., Gurrin, C. (eds.) MMM 2013, Part II. LNCS, vol. 7733, pp. 547–549. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Scott, D., et al.: DCU at MMM 2013 Video Browser Showdown. In: Li, S., El Saddik, A., Wang, M., Mei, T., Sebe, N., Yan, S., Hong, R., Gurrin, C. (eds.) MMM 2013, Part II. LNCS, vol. 7733, pp. 541–543. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Bai, H., Wang, L., Dong, Y., Tao, K.: Interactive Video Retrieval Using Combination of Semantic Index and Instance Search. In: Li, S., El Saddik, A., Wang, M., Mei, T., Sebe, N., Yan, S., Hong, R., Gurrin, C. (eds.) MMM 2013, Part II. LNCS, vol. 7733, pp. 554–556. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Viola, P., Jones, M.: Robust Real-time Face Detection. International Journal of Computer Vision (IJCV), 137–154 (2004)Google Scholar
  5. 5.
    Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object Detection with Discriminatively Trained Part Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 1627–1645 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thanh Duc Ngo
    • 1
    • 2
  • Vu Hoang Nguyen
    • 1
  • Vu Lam
    • 1
    • 2
  • Sang Phan
    • 4
  • Duy-Dinh Le
    • 3
  • Duc Anh Duong
    • 1
  • Shin’ichi Satoh
    • 3
  1. 1.University of Information Technology - VNU HCMCVietnam
  2. 2.University of Science - VNU HCMCVietnam
  3. 3.National Institute of InformaticsJapan
  4. 4.The Graduate University for Advanced StudiesJapan

Personalised recommendations